Overview

Dataset statistics

Number of variables13
Number of observations30733
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 MiB
Average record size in memory105.0 B

Variable types

Boolean1
Numeric11
Categorical1

Alerts

Gender has constant value ""Constant
PositionBinned has constant value ""Constant
SwimTime is highly overall correlated with SwimPositionPercentageHigh correlation
BikeTime is highly overall correlated with FinishTime and 1 other fieldsHigh correlation
RunTime is highly overall correlated with FinishTime and 1 other fieldsHigh correlation
FinishTime is highly overall correlated with BikeTime and 1 other fieldsHigh correlation
SwimPositionPercentage is highly overall correlated with SwimTimeHigh correlation
BikePositionPercentage is highly overall correlated with BikeTimeHigh correlation
RunPositionPercentage is highly overall correlated with RunTimeHigh correlation

Reproduction

Analysis started2023-10-26 02:21:44.323451
Analysis finished2023-10-26 02:21:57.488840
Duration13.17 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Gender
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size270.1 KiB
False
30733 
ValueCountFrequency (%)
False 30733
100.0%
2023-10-25T20:21:57.569282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

SwimTime
Real number (ℝ)

HIGH CORRELATION 

Distinct1876
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2160.7682
Minimum1321
Maximum4054
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size480.2 KiB
2023-10-25T20:21:57.694141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1321
5-th percentile1676
Q11943
median2142
Q32356
95-th percentile2704
Maximum4054
Range2733
Interquartile range (IQR)413

Descriptive statistics

Standard deviation317.73635
Coefficient of variation (CV)0.14704786
Kurtosis0.68341215
Mean2160.7682
Median Absolute Deviation (MAD)206
Skewness0.44345176
Sum66406890
Variance100956.39
MonotonicityNot monotonic
2023-10-25T20:21:57.836400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1999 59
 
0.2%
2160 54
 
0.2%
2193 54
 
0.2%
1980 53
 
0.2%
2132 53
 
0.2%
2099 52
 
0.2%
2261 52
 
0.2%
2086 52
 
0.2%
2067 52
 
0.2%
2239 52
 
0.2%
Other values (1866) 30200
98.3%
ValueCountFrequency (%)
1321 1
 
< 0.1%
1322 3
< 0.1%
1326 1
 
< 0.1%
1327 1
 
< 0.1%
1328 2
< 0.1%
1329 2
< 0.1%
1330 3
< 0.1%
1334 4
< 0.1%
1335 1
 
< 0.1%
1336 1
 
< 0.1%
ValueCountFrequency (%)
4054 1
< 0.1%
4002 1
< 0.1%
3998 1
< 0.1%
3794 1
< 0.1%
3708 1
< 0.1%
3685 1
< 0.1%
3680 1
< 0.1%
3661 1
< 0.1%
3656 1
< 0.1%
3636 1
< 0.1%

Transition1Time
Real number (ℝ)

Distinct448
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean232.70921
Minimum46
Maximum499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size480.2 KiB
2023-10-25T20:21:58.072481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile123.6
Q1179
median227
Q3278
95-th percentile365
Maximum499
Range453
Interquartile range (IQR)99

Descriptive statistics

Standard deviation73.618578
Coefficient of variation (CV)0.31635439
Kurtosis0.14366229
Mean232.70921
Median Absolute Deviation (MAD)49
Skewness0.49080644
Sum7151852
Variance5419.695
MonotonicityNot monotonic
2023-10-25T20:21:58.220708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
216 191
 
0.6%
221 191
 
0.6%
230 187
 
0.6%
218 185
 
0.6%
238 184
 
0.6%
228 182
 
0.6%
197 181
 
0.6%
222 181
 
0.6%
198 181
 
0.6%
201 180
 
0.6%
Other values (438) 28890
94.0%
ValueCountFrequency (%)
46 1
 
< 0.1%
48 1
 
< 0.1%
50 1
 
< 0.1%
51 1
 
< 0.1%
53 4
< 0.1%
54 1
 
< 0.1%
57 1
 
< 0.1%
58 4
< 0.1%
60 1
 
< 0.1%
61 4
< 0.1%
ValueCountFrequency (%)
499 3
< 0.1%
498 2
 
< 0.1%
497 5
< 0.1%
496 3
< 0.1%
495 4
< 0.1%
494 1
 
< 0.1%
493 1
 
< 0.1%
492 3
< 0.1%
491 4
< 0.1%
490 2
 
< 0.1%

BikeTime
Real number (ℝ)

HIGH CORRELATION 

Distinct3550
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10299.278
Minimum7714
Maximum13588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size480.2 KiB
2023-10-25T20:21:58.358919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7714
5-th percentile9327
Q19866
median10250
Q310685
95-th percentile11459.4
Maximum13588
Range5874
Interquartile range (IQR)819

Descriptive statistics

Standard deviation655.48869
Coefficient of variation (CV)0.063644139
Kurtosis0.74415144
Mean10299.278
Median Absolute Deviation (MAD)407
Skewness0.41982164
Sum3.1652772 × 108
Variance429665.43
MonotonicityNot monotonic
2023-10-25T20:21:58.501086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9933 32
 
0.1%
9979 32
 
0.1%
10124 32
 
0.1%
9917 32
 
0.1%
10226 32
 
0.1%
10034 31
 
0.1%
9947 31
 
0.1%
10391 31
 
0.1%
10014 30
 
0.1%
9945 30
 
0.1%
Other values (3540) 30420
99.0%
ValueCountFrequency (%)
7714 1
< 0.1%
7800 1
< 0.1%
7882 1
< 0.1%
7982 1
< 0.1%
7989 1
< 0.1%
7997 1
< 0.1%
8009 1
< 0.1%
8014 1
< 0.1%
8016 2
< 0.1%
8035 1
< 0.1%
ValueCountFrequency (%)
13588 1
< 0.1%
13264 1
< 0.1%
13239 1
< 0.1%
13152 1
< 0.1%
13133 1
< 0.1%
13125 1
< 0.1%
13117 1
< 0.1%
13110 1
< 0.1%
13055 1
< 0.1%
13038 1
< 0.1%

Transition2Time
Real number (ℝ)

Distinct421
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166.25757
Minimum46
Maximum499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size480.2 KiB
2023-10-25T20:21:58.648334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile85
Q1121
median156
Q3200
95-th percentile283
Maximum499
Range453
Interquartile range (IQR)79

Descriptive statistics

Standard deviation62.293902
Coefficient of variation (CV)0.37468309
Kurtosis1.6005474
Mean166.25757
Median Absolute Deviation (MAD)39
Skewness1.0314527
Sum5109594
Variance3880.5302
MonotonicityNot monotonic
2023-10-25T20:21:58.793921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129 257
 
0.8%
132 246
 
0.8%
137 242
 
0.8%
148 239
 
0.8%
126 237
 
0.8%
133 236
 
0.8%
146 235
 
0.8%
149 235
 
0.8%
145 232
 
0.8%
124 230
 
0.7%
Other values (411) 28344
92.2%
ValueCountFrequency (%)
46 3
 
< 0.1%
47 2
 
< 0.1%
48 2
 
< 0.1%
49 4
 
< 0.1%
50 6
< 0.1%
51 8
< 0.1%
52 10
< 0.1%
53 9
< 0.1%
54 9
< 0.1%
55 9
< 0.1%
ValueCountFrequency (%)
499 1
 
< 0.1%
498 4
< 0.1%
495 2
< 0.1%
488 1
 
< 0.1%
487 1
 
< 0.1%
486 2
< 0.1%
485 2
< 0.1%
483 2
< 0.1%
482 2
< 0.1%
480 3
< 0.1%

RunTime
Real number (ℝ)

HIGH CORRELATION 

Distinct3219
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6784.153
Minimum4332
Maximum11211
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size480.2 KiB
2023-10-25T20:21:58.933056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4332
5-th percentile5850
Q16350
median6748
Q37166
95-th percentile7865
Maximum11211
Range6879
Interquartile range (IQR)816

Descriptive statistics

Standard deviation616.13025
Coefficient of variation (CV)0.090819038
Kurtosis0.33967213
Mean6784.153
Median Absolute Deviation (MAD)407
Skewness0.39850419
Sum2.0849737 × 108
Variance379616.49
MonotonicityNot monotonic
2023-10-25T20:21:59.085118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6780 33
 
0.1%
6686 33
 
0.1%
6600 32
 
0.1%
6777 32
 
0.1%
6660 31
 
0.1%
6754 31
 
0.1%
6417 31
 
0.1%
6764 31
 
0.1%
6340 30
 
0.1%
6853 30
 
0.1%
Other values (3209) 30419
99.0%
ValueCountFrequency (%)
4332 1
< 0.1%
4387 1
< 0.1%
4452 1
< 0.1%
4494 1
< 0.1%
4758 1
< 0.1%
4771 1
< 0.1%
4813 1
< 0.1%
4828 1
< 0.1%
4860 1
< 0.1%
4882 1
< 0.1%
ValueCountFrequency (%)
11211 1
< 0.1%
10124 1
< 0.1%
9930 1
< 0.1%
9589 1
< 0.1%
9462 1
< 0.1%
9458 1
< 0.1%
9454 1
< 0.1%
9391 1
< 0.1%
9384 1
< 0.1%
9324 1
< 0.1%

FinishTime
Real number (ℝ)

HIGH CORRELATION 

Distinct4770
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19643.179
Minimum15253
Maximum24514
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size480.2 KiB
2023-10-25T20:21:59.238257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15253
5-th percentile18083
Q119002
median19630
Q320288
95-th percentile21245
Maximum24514
Range9261
Interquartile range (IQR)1286

Descriptive statistics

Standard deviation968.43641
Coefficient of variation (CV)0.04930141
Kurtosis0.37490616
Mean19643.179
Median Absolute Deviation (MAD)643
Skewness0.0027545841
Sum6.0369382 × 108
Variance937869.08
MonotonicityNot monotonic
2023-10-25T20:21:59.383339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19633 26
 
0.1%
20077 24
 
0.1%
19399 23
 
0.1%
19959 23
 
0.1%
19547 23
 
0.1%
19829 23
 
0.1%
18929 22
 
0.1%
19412 22
 
0.1%
19880 22
 
0.1%
19567 22
 
0.1%
Other values (4760) 30503
99.3%
ValueCountFrequency (%)
15253 1
< 0.1%
15475 1
< 0.1%
15553 1
< 0.1%
15696 1
< 0.1%
15738 1
< 0.1%
15816 1
< 0.1%
15911 1
< 0.1%
15920 1
< 0.1%
15932 1
< 0.1%
15945 1
< 0.1%
ValueCountFrequency (%)
24514 1
< 0.1%
24358 1
< 0.1%
24231 1
< 0.1%
23710 1
< 0.1%
23704 1
< 0.1%
23633 1
< 0.1%
23532 1
< 0.1%
23474 1
< 0.1%
23442 1
< 0.1%
23344 1
< 0.1%

Position
Real number (ℝ)

Distinct604
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.333127
Minimum1
Maximum427
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size480.2 KiB
2023-10-25T20:21:59.527074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17
Q138
median60
Q390
95-th percentile145
Maximum427
Range426
Interquartile range (IQR)52

Descriptive statistics

Standard deviation46.683963
Coefficient of variation (CV)0.67332839
Kurtosis9.5998486
Mean69.333127
Median Absolute Deviation (MAD)25
Skewness2.2631223
Sum2130815
Variance2179.3924
MonotonicityNot monotonic
2023-10-25T20:21:59.674424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43 356
 
1.2%
39 352
 
1.1%
40 351
 
1.1%
42 351
 
1.1%
41 351
 
1.1%
38 348
 
1.1%
44 344
 
1.1%
46 340
 
1.1%
35 338
 
1.1%
45 338
 
1.1%
Other values (594) 27264
88.7%
ValueCountFrequency (%)
1 6
 
< 0.1%
2 3
 
< 0.1%
3 7
 
< 0.1%
4 18
 
0.1%
5 32
 
0.1%
6 40
 
0.1%
7 59
0.2%
8 73
0.2%
9 91
0.3%
10 103
0.3%
ValueCountFrequency (%)
427 1
< 0.1%
425.5 2
< 0.1%
424 1
< 0.1%
423 1
< 0.1%
421.5 2
< 0.1%
420 1
< 0.1%
419 1
< 0.1%
418 1
< 0.1%
417 1
< 0.1%
416 1
< 0.1%

PositionPercentage
Real number (ℝ)

Distinct12287
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17527611
Minimum0.10012674
Maximum0.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size480.2 KiB
2023-10-25T20:21:59.817118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.10012674
5-th percentile0.10763889
Q10.13768116
median0.17525773
Q30.21283784
95-th percentile0.24289392
Maximum0.25
Range0.14987326
Interquartile range (IQR)0.075156678

Descriptive statistics

Standard deviation0.043406519
Coefficient of variation (CV)0.24764652
Kurtosis-1.1998109
Mean0.17527611
Median Absolute Deviation (MAD)0.037576573
Skewness4.3144831 × 10-5
Sum5386.7608
Variance0.0018841259
MonotonicityNot monotonic
2023-10-25T20:21:59.974578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.25 193
 
0.6%
0.2 147
 
0.5%
0.1666666667 126
 
0.4%
0.125 107
 
0.3%
0.1428571429 100
 
0.3%
0.1818181818 76
 
0.2%
0.2222222222 75
 
0.2%
0.1111111111 75
 
0.2%
0.1538461538 56
 
0.2%
0.2307692308 56
 
0.2%
Other values (12277) 29722
96.7%
ValueCountFrequency (%)
0.1001267427 1
< 0.1%
0.1001642036 1
< 0.1%
0.1001669449 2
< 0.1%
0.1001727116 1
< 0.1%
0.1001821494 1
< 0.1%
0.1001855288 2
< 0.1%
0.1001926782 1
< 0.1%
0.1002024291 2
< 0.1%
0.100204499 1
< 0.1%
0.1002087683 1
< 0.1%
ValueCountFrequency (%)
0.25 193
0.6%
0.2498210451 1
 
< 0.1%
0.2497985496 1
 
< 0.1%
0.2497076023 1
 
< 0.1%
0.2496831432 1
 
< 0.1%
0.2496413199 2
 
< 0.1%
0.2496307238 1
 
< 0.1%
0.2496240602 1
 
< 0.1%
0.2496194825 1
 
< 0.1%
0.2496 1
 
< 0.1%

PositionBinned
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size270.3 KiB
10_25
30733 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters153665
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10_25
2nd row10_25
3rd row10_25
4th row10_25
5th row10_25

Common Values

ValueCountFrequency (%)
10_25 30733
100.0%

Length

2023-10-25T20:22:00.115849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-25T20:22:00.214181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10_25 30733
100.0%

Most occurring characters

ValueCountFrequency (%)
1 30733
20.0%
0 30733
20.0%
_ 30733
20.0%
2 30733
20.0%
5 30733
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 122932
80.0%
Connector Punctuation 30733
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 30733
25.0%
0 30733
25.0%
2 30733
25.0%
5 30733
25.0%
Connector Punctuation
ValueCountFrequency (%)
_ 30733
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 153665
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 30733
20.0%
0 30733
20.0%
_ 30733
20.0%
2 30733
20.0%
5 30733
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 153665
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 30733
20.0%
0 30733
20.0%
_ 30733
20.0%
2 30733
20.0%
5 30733
20.0%

SwimPositionPercentage
Real number (ℝ)

HIGH CORRELATION 

Distinct21324
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29224303
Minimum0.0012674271
Maximum0.98905109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size480.2 KiB
2023-10-25T20:22:00.333277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0012674271
5-th percentile0.043566144
Q10.14186047
median0.25641026
Q30.40936556
95-th percentile0.66248147
Maximum0.98905109
Range0.98778367
Interquartile range (IQR)0.26750509

Descriptive statistics

Standard deviation0.19124765
Coefficient of variation (CV)0.65441305
Kurtosis0.11267815
Mean0.29224303
Median Absolute Deviation (MAD)0.12848183
Skewness0.78416209
Sum8981.505
Variance0.036575664
MonotonicityNot monotonic
2023-10-25T20:22:00.489377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.25 63
 
0.2%
0.2 55
 
0.2%
0.5 51
 
0.2%
0.3333333333 49
 
0.2%
0.1666666667 44
 
0.1%
0.125 39
 
0.1%
0.1428571429 33
 
0.1%
0.375 29
 
0.1%
0.1 25
 
0.1%
0.08333333333 25
 
0.1%
Other values (21314) 30320
98.7%
ValueCountFrequency (%)
0.001267427123 1
< 0.1%
0.001706484642 1
< 0.1%
0.001818181818 1
< 0.1%
0.00185528757 1
< 0.1%
0.001893939394 1
< 0.1%
0.001901140684 1
< 0.1%
0.002024291498 1
< 0.1%
0.002403846154 1
< 0.1%
0.002444987775 1
< 0.1%
0.00253164557 1
< 0.1%
ValueCountFrequency (%)
0.9890510949 1
< 0.1%
0.9869109948 1
< 0.1%
0.9851485149 1
< 0.1%
0.9814385151 1
< 0.1%
0.9810126582 1
< 0.1%
0.9803921569 1
< 0.1%
0.9791666667 1
< 0.1%
0.9782608696 1
< 0.1%
0.9754385965 1
< 0.1%
0.9723076923 1
< 0.1%

BikePositionPercentage
Real number (ℝ)

HIGH CORRELATION 

Distinct17655
Distinct (%)57.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20462208
Minimum0.0018552876
Maximum0.93548387
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size480.2 KiB
2023-10-25T20:22:00.640068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0018552876
5-th percentile0.070824698
Q10.13114754
median0.18891688
Q30.26035503
95-th percentile0.39183138
Maximum0.93548387
Range0.93362858
Interquartile range (IQR)0.12920749

Descriptive statistics

Standard deviation0.1006
Coefficient of variation (CV)0.49163802
Kurtosis1.5360187
Mean0.20462208
Median Absolute Deviation (MAD)0.063419572
Skewness0.97565262
Sum6288.6505
Variance0.010120359
MonotonicityNot monotonic
2023-10-25T20:22:00.790883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 96
 
0.3%
0.25 95
 
0.3%
0.1666666667 83
 
0.3%
0.125 74
 
0.2%
0.1428571429 66
 
0.2%
0.1818181818 49
 
0.2%
0.2222222222 41
 
0.1%
0.1111111111 41
 
0.1%
0.1538461538 40
 
0.1%
0.3333333333 40
 
0.1%
Other values (17645) 30108
98.0%
ValueCountFrequency (%)
0.00185528757 1
< 0.1%
0.003773584906 1
< 0.1%
0.005882352941 1
< 0.1%
0.006493506494 1
< 0.1%
0.007434944238 1
< 0.1%
0.007853403141 1
< 0.1%
0.008097165992 1
< 0.1%
0.008174386921 1
< 0.1%
0.008849557522 1
< 0.1%
0.009063444109 1
< 0.1%
ValueCountFrequency (%)
0.935483871 1
< 0.1%
0.8885135135 1
< 0.1%
0.8081534772 1
< 0.1%
0.7943313953 1
< 0.1%
0.7924528302 1
< 0.1%
0.78962818 1
< 0.1%
0.7716535433 1
< 0.1%
0.7641025641 1
< 0.1%
0.763546798 1
< 0.1%
0.7603305785 1
< 0.1%

RunPositionPercentage
Real number (ℝ)

HIGH CORRELATION 

Distinct17781
Distinct (%)57.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20811018
Minimum0.0025773196
Maximum0.88613861
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size480.2 KiB
2023-10-25T20:22:01.030595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0025773196
5-th percentile0.071428571
Q10.13407821
median0.19339243
Q30.26684636
95-th percentile0.39485819
Maximum0.88613861
Range0.88356129
Interquartile range (IQR)0.13276815

Descriptive statistics

Standard deviation0.10057132
Coefficient of variation (CV)0.48325995
Kurtosis0.99089733
Mean0.20811018
Median Absolute Deviation (MAD)0.065485449
Skewness0.82968844
Sum6395.8503
Variance0.01011459
MonotonicityNot monotonic
2023-10-25T20:22:01.177013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.25 103
 
0.3%
0.2 87
 
0.3%
0.1666666667 87
 
0.3%
0.125 67
 
0.2%
0.1428571429 63
 
0.2%
0.1818181818 53
 
0.2%
0.3333333333 48
 
0.2%
0.2222222222 45
 
0.1%
0.1538461538 44
 
0.1%
0.1111111111 38
 
0.1%
Other values (17771) 30098
97.9%
ValueCountFrequency (%)
0.002577319588 1
< 0.1%
0.00272479564 1
< 0.1%
0.002849002849 1
< 0.1%
0.003076923077 1
< 0.1%
0.003378378378 1
< 0.1%
0.003436426117 1
< 0.1%
0.004524886878 1
< 0.1%
0.005063291139 1
< 0.1%
0.005128205128 1
< 0.1%
0.005291005291 2
< 0.1%
ValueCountFrequency (%)
0.8861386139 1
< 0.1%
0.8770491803 1
< 0.1%
0.8465346535 1
< 0.1%
0.7871287129 1
< 0.1%
0.7722772277 1
< 0.1%
0.7619047619 1
< 0.1%
0.7376237624 1
< 0.1%
0.7333333333 1
< 0.1%
0.7178217822 1
< 0.1%
0.7128712871 1
< 0.1%

Interactions

2023-10-25T20:21:56.107724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:45.648479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:46.698897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:47.700684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:48.798460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:49.842154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:50.936149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:51.945693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:52.931084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:54.088882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:55.111649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:56.201279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:45.760339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:46.789026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:47.792685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:48.891413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:49.948208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:51.029047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:52.035243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:53.027852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:54.186028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:55.204221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:56.287960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:45.853537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:46.874844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:47.881377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:48.979971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:50.048632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:51.117812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:52.120080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:53.122947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:54.276679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:55.292466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:56.377992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:45.946921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:46.966670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:47.972129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:49.078293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:50.148535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:51.215561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:52.212471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:53.222980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:54.369007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:55.381683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:56.465305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:46.037668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:47.054895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:48.061848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:49.171376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:50.247293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:51.304397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:52.298233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:53.316243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:54.459255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:55.469450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:56.562894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:46.138441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:47.154874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:48.161491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:49.274133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:50.350495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:51.403295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:52.394992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:53.420259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:54.559206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:55.567180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:56.653607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:46.230193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:47.243905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:48.251834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:49.362551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:50.444872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:51.489865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:52.480368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:53.601123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:54.648390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:55.656771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:56.741872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:46.319357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:47.330783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:48.340503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:49.464635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:50.538296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:51.575579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:52.565563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:53.696663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:54.740017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:55.743441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:56.842481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:46.422910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:47.430592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:48.443958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:49.566935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:50.645398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:51.676499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:52.665400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:53.801242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:54.841985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:55.843481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:56.934365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:46.517611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:47.522688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:48.615127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:49.662292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:50.745257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:51.769266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:52.756692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:53.898410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:54.933520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:55.933439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:57.023163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:46.606558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:47.610839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:48.708303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:49.751773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:50.840213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:51.857886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:52.843586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:53.992166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:55.023269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-25T20:21:56.020157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-10-25T20:22:01.277555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SwimTimeTransition1TimeBikeTimeTransition2TimeRunTimeFinishTimePositionPositionPercentageSwimPositionPercentageBikePositionPercentageRunPositionPercentage
SwimTime1.0000.045-0.0140.0690.0590.3440.0990.1740.731-0.056-0.162
Transition1Time0.0451.0000.1350.436-0.0360.1850.0920.1640.1820.090-0.038
BikeTime-0.0140.1351.0000.093-0.0190.6310.1680.284-0.0330.576-0.172
Transition2Time0.0690.4360.0931.0000.1270.2630.1610.1780.0920.0820.018
RunTime0.059-0.036-0.0190.1271.0000.6320.3130.303-0.120-0.1860.645
FinishTime0.3440.1850.6310.2630.6321.0000.3640.4770.1530.2470.241
Position0.0990.0920.1680.1610.3130.3641.0000.4010.1150.2050.188
PositionPercentage0.1740.1640.2840.1780.3030.4770.4011.0000.2280.4720.453
SwimPositionPercentage0.7310.182-0.0330.092-0.1200.1530.1150.2281.000-0.074-0.215
BikePositionPercentage-0.0560.0900.5760.082-0.1860.2470.2050.472-0.0741.000-0.297
RunPositionPercentage-0.162-0.038-0.1720.0180.6450.2410.1880.453-0.215-0.2971.000

Missing values

2023-10-25T20:21:57.156275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-25T20:21:57.376947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

GenderSwimTimeTransition1TimeBikeTimeTransition2TimeRunTimeFinishTimePositionPositionPercentagePositionBinnedSwimPositionPercentageBikePositionPercentageRunPositionPercentage
21F2457252999827664791946244.00.15827310_250.3075540.1726620.172662
170F22051281056016671642022310.00.15151510_250.2424240.1515150.166667
172F24811761066918572122072388.00.24043710_250.4234970.2704920.256831
462F276110810217886370195443.00.13043510_250.7391300.3913040.086957
567F24702761144417270162137844.00.14864910_250.5625000.1418920.206081
597F262432910565123640920050135.00.21844710_250.7281550.2556630.118123
615F2201175958713368711896773.00.12166710_250.3116670.0466670.200000
633F224617110848131714920545103.00.18761410_250.2823320.2495450.194900
646F1902277993817181822047037.00.21264410_250.0747130.2011490.402299
696F22543081005414774452020831.00.19375010_250.3437500.1500000.243750
GenderSwimTimeTransition1TimeBikeTimeTransition2TimeRunTimeFinishTimePositionPositionPercentagePositionBinnedSwimPositionPercentageBikePositionPercentageRunPositionPercentage
839836F16912191034117060831850425.00.14367810_250.0775860.1724140.149425
839888F20973651083228770832066455.00.20522410_250.2630600.1641790.320896
839899F14992201027411562461835451.00.12911410_250.0556960.2784810.074684
839940F176611094539170451846513.00.16250010_250.0750000.1250000.200000
839941F2274146994412470441953218.00.22500010_250.2875000.1875000.187500
839998F23883081118016269842102239.00.13175710_250.4594590.0743240.202703
840023F24423871079119268512066377.00.22383710_250.5421510.3095930.174419
840041F20562181152714770722102084.00.24852110_250.1035500.3994080.269231
840055F19233051208430470352165161.00.19303810_250.0806960.2563290.205696
840073F21001931028023361481895437.00.19371710_250.3272250.2696340.178010